code: def initialize_model(
      model: nn.Module,
      meta_args: Dict[str, torch.Tensor],
      device_mesh: DeviceMesh,
      memory_budget: float = -1.0,
      overlap: bool = False,
      solver_preference: str = "standard",
      dataloader_option: str = "replicated",
      shard_option: str = "standard",
      save_solver_solution: bool = False,
      load_solver_solution: bool = False,
      solution_path: str = None,
      return_solution: bool = False,
  ):
      """
      This method is used to initialize the sharded model which could be used as normal pytorch model.
  
      Args:
          model: the model to be sharded.
          meta_args: the meta_args is used to specify the input shapes of the model.
          device_mesh: the device mesh to execute the model.
          memory_budget(optional): the max cuda memory could be used. If the memory budget is -1.0,
              the memory budget will be infinity.
          overlap(optional): the overlap is used to specify whether to overlap gradient communication and
              backward computing.
          solver_preference(optional): the solver_preference is used to specify which parallelism algorithm
              has higher priority. The valid solver_preference could be 'standard', 'tp', or 'dp'.
          dataloader_option(optional): the dataloader_option is used to specify which kind of data_loader will
              be used. The valid dataloader_option could be 'replicated' or 'distributed'.
          shard_option(optional): the shard_option is used to specify how many axes will be used to shard the
              model. The valid shard_option could be 'standard', 'shard', 'shard_last_axis', or 'full_shard'.
          save_solver_solution(optional): if the save_solver_solution is True, the solution will be saved
              to the solution_path.
          load_solver_solution(optional): if the load_solver_solution is True, the solution will be loaded
              from the solution_path.
          solution_path(optional): the path to save or load the solution.
          return_solution(optional): if the return_solution is True, the solution will be returned. The returned
              solution will be used to debug or help to analyze the sharding result. Therefore, we will not just
              return a series of integers, but return the best strategies.
      """
      tracer = ColoTracer(trace_act_ckpt=True, bias_addition_split=True)
  
      graph = tracer.trace(root=model, meta_args=meta_args)
      graph.set_codegen(ActivationCheckpointCodeGen())
      gm = ColoGraphModule(model, graph, model.__class__.__name__)
  
      shape_prop_pass(gm, *meta_args.values())
      gm.recompile()
  
      strategies_constructor = build_strategy_constructor(
          graph,
          device_mesh,
          solver_preference=solver_preference,
          dataloader_option=dataloader_option,
          shard_option=shard_option,
      )
      if load_solver_solution:
          solution = torch.load(solution_path)
      else:
          solution = solve_solution(
              gm, strategies_constructor, memory_budget
          )
          if save_solver_solution:
              torch.save(solution, solution_path)
  
      gm, sharding_spec_dicts = transform_to_sharded_model(
          gm,
          meta_args,
          solution,
          device_mesh,
          strategies_constructor,
          overlap,
      )
  
      model_to_return = ModuleWrapper(gm, *sharding_spec_dicts)
  
      if return_solution:
          solution_to_return = []
          nodes = [
              strategies_vector.node
              for strategies_vector in strategies_constructor.leaf_strategies
          ]
          for index, node in enumerate(nodes):
              solution_to_return.append(
                  f"{node.name} {node.strategies_vector[solution[index]].name}"
              )
          return model_to_return, solution_to_return
      else:
          return model_to_return
  
imports: from typing import Dict, List, Tuple
  import torch
  import torch.distributed
  import torch.nn
  from torch.fx.graph import Graph
  from colossalai._analyzer.fx.codegen import ActivationCheckpointCodeGen
  from colossalai._analyzer.fx.graph_module import ColoGraphModule
  from colossalai._analyzer.fx.passes import shape_prop_pass
  from colossalai._analyzer.fx.tracer.tracer import ColoTracer
  from colossalai.auto_parallel.passes.runtime_apply_pass import runtime_apply_pass
  from colossalai.auto_parallel.passes.runtime_preparation_pass import runtime_preparation_pass
  from colossalai.auto_parallel.tensor_shard.options import DataloaderOption, ShardOption, SolverOptions, SolverPerference
  from colossalai.auto_parallel.tensor_shard.sharding_strategy import CommAction
  from colossalai.auto_parallel.tensor_shard.solver import CostGraph, Solver, StrategiesConstructor
  from colossalai.device.alpha_beta_profiler import AlphaBetaProfiler
  from colossalai.device.device_mesh import DeviceMesh
  from colossalai.tensor.sharding_spec import ShardingSpec
label_desc: Use image node_id 7 for calling a global function with example usage: initialize_model(model, meta_args, device_mesh, memory_budget, overlap, solver_preference, dataloader_option, shard_option, save_solver_solution, load_solver_solution, solution_path, return_solution) and returns: model_to_return,  solution_to_return, model_to_return